Umer Muhammad Junaid, Amin Javeria, Sharif Muhammad, Anjum Muhammad Almas, Azam Faisal, Shah Jamal Hussain
Department of Computer Science Comsats University Islamabad, Wah Campus Rawalpindi Pakistan.
Department of Computer Science University of Wah Rawalpindi Pakistan.
Concurr Comput. 2022 Sep 10;34(20):e6434. doi: 10.1002/cpe.6434. Epub 2021 Jun 29.
COVID-19 is a quickly spreading over 10 million persons globally. The overall number of infected patients worldwide is estimated to be around 133,381,413 people. Infection rate is being increased on daily basis. It has also caused a devastating effect on the world economy and public health. Early stage detection of this disease is mandatory to reduce the mortality rate. Artificial intelligence performs a vital role for COVID-19 detection at an initial stage using chest radiographs. The proposed methods comprise of the two phases. Deep features (DFs) are derived from its last fully connected layers of pre-trained models like AlexNet and MobileNet in phase-I. Later these feature vectors are fused serially. Best features are selected through feature selection method of PCA and passed to the SVM and KNN for classification. In phase-II, quantum transfer learning model is utilized, in which a pre-trained ResNet-18 model is applied for DF collection and then these features are supplied as an input to the 4-qubit quantum circuit for model training with the tuned hyperparameters. The proposed technique is evaluated on two publicly available x-ray imaging datasets. The proposed methodology achieved an accuracy index of 99.0% with three classes including corona virus-positive images, normal images, and pneumonia radiographs. In comparison to other recently published work, the experimental findings show that the proposed approach outperforms it.
新型冠状病毒肺炎正在全球迅速传播，感染人数超过1000万。全球感染患者总数估计约为133381413人。感染率每天都在上升。它还对世界经济和公共卫生造成了毁灭性影响。早期检测这种疾病对于降低死亡率至关重要。人工智能在使用胸部X光片对新型冠状病毒肺炎进行早期检测中发挥着至关重要的作用。所提出的方法包括两个阶段。在第一阶段，从预训练模型（如AlexNet和MobileNet）的最后全连接层中提取深度特征（DFs）。然后将这些特征向量依次融合。通过主成分分析（PCA）的特征选择方法选择最佳特征，并将其传递给支持向量机（SVM）和K近邻（KNN）进行分类。在第二阶段，利用量子迁移学习模型，其中应用预训练的ResNet-18模型收集深度特征，然后将这些特征作为输入提供给4量子比特量子电路，使用调整后的超参数进行模型训练。在所提出的技术在两个公开可用的X光成像数据集上进行了评估。所提出的方法在包括冠状病毒阳性图像、正常图像和肺炎X光片的三类图像上实现了99.0%的准确率指标。与其他最近发表的工作相比，实验结果表明所提出的方法优于其他方法。
